Students dropout and success | Net Phi: Internet, Society, and Philosophy

Examining student drop out rates and taking a deeper look into the factors that contribute to both this and overall academic success can be very beneficial to understanding the factors that are important and necessary for students to be able to thrive in their academic careers. If we can determine statistically what factors have the greatest impact we can examine how these factors come into play within peoples lives from philosophical perspective. Some factors we will look into are not limited to but include family income levels, location of where students are from and live, as well as other factors. These factors are crucial in determining who factors lead to students potentially falling behind academically and not being able to catch up as well as students potentially losing any academic motivation and aspirations that they once may have had.

In this project we used the format anatomies elements method to read and analyze the data set. This method focuses on what, when, how, and why the topic is being studied and discussed. We chose this method because it would question the data and the reason why it was collected, and we could make answers . Based on the information provided, we found that the data collected focuses on students in Portugal 2021 and their academic successes. The data was collected under the SATDAP program in Portugal with the sole purpose of supporting students. The data also provided the outcomes of each student, whether they dropped out, enrolled, or graduated.

Through our format anatomy analysis, we identified that the current data structure reinforces socioeconomic determinism by weighting factors outside institutional influence while ignoring student agency and personal circumstances. Our bias analysis uncovered significant blind spots including the absence of mental health indicators, learning style accommodations, and work-life balance factors. Most critically, we found that the dataset lacks variables capturing student agency factors like study habits, help-seeking behaviors, goal-setting practices, and resilience strategies that students can actively control. Without these elements, predictive models risk becoming self-fulfilling prophecies that label students as "at-risk" based solely on background characteristics rather than actionable behaviors.

Our proposed improvements focus on three key areas: incorporating student agency variables, capturing comprehensive life context including care responsibilities and employment obligations, and documenting institutional experience quality through support service utilization.

Term

Spring 2025